8A.4 Tests of a cycled EnKF data assimilation and forecasts for the 10 May 2010 tornado outbreak in the central US domain

Tuesday, 6 November 2012: 4:15 PM
Symphony I (Loews Vanderbilt Hotel)
Youngsun Jung, CAPS/Univ. of Oklahoma, Norman, OK; and M. Xue, Y. Wang, Y. Pan, and K. Zhu

We applied the parallel-ensemble Kalman filter (EnKF) algorithm based on domain decomposition to the May 10, 2010 Oklahoma-Kansas tornado outbreak case that spawned over 60 tornadoes to test the storm-scale, cycled EnKF DA, and ensemble forecasts. To include both mesoscale and storm-scale features important on this day, the storm-scale ensemble with 4-km horizontal grid spacing was nested inside the regional WRF ensemble analyses at a 40-km. The storm-scale domain was 1750 × 1920 × 21 km3 and is the same as the CAPS VORTEX2 realtime forecasting domain. The 40-menber regional ensemble was interpolated to form a storm-scale ensemble at 1500 UTC on 10 May 2010. The boundary conditions for each ensemble member were provided by the corresponding regional ensemble member. In the storm-scale ensemble, the ARPS system was used in both simulation and analysis. Using the MPI-OpenMP hybrid ARPS EnSRF, conventional (surface, sounding, profiler) and radar data were assimilated every hour. Additionally, surface and radar data were assimilated every ten minutes during the last one-hour period before the forecast was launched. Finally, forward ensemble and deterministic forecasts were launched every three hours starting from 1800 UTC for 6 hours. The preliminary results showed the parallel EnKF algorithm exhibited good scalability for dense radar observations. The analyzed base reflectivity mosaic at the end of each assimilation window exhibited a good fit with the observations in shape, structure, and intensity. Also, a line of strong, isolated storms in the central Kansas and Oklahoma was captured reasonably well by the ensemble forecasts throughout the forecast period.
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